Like Python, R has its own built-in build system.
The R build system is remarkably uniform and well-tested. This makes it one of the easiest build systems to create new Spack packages for.
RPackage base classes have a single phase:
install- install the package
By default, this phase runs the following command:
$ R CMD INSTALL --library=/path/to/installation/prefix/rlib/R/library .
Finding R packages
The vast majority of R packages are hosted on CRAN - The Comprehensive R Archive Network. If you are looking for a particular R package, search for “CRAN <package-name>” and you should quickly find what you want. If it isn’t on CRAN, try Bioconductor, another common R repository.
For the purposes of this tutorial, we will be walking through
as an example. If you search for “CRAN caret”, you will quickly find what
you are looking for at https://cran.r-project.org/package=caret.
https://cran.r-project.org is the main CRAN website. However, CRAN also
has a https://cloud.r-project.org site that automatically redirects to
mirrors around the world.
For stability and performance reasons, we will use https://cloud.r-project.org/package=caret.
If you search for “Package source”, you will find the download URL for
the latest release. Use this URL with
spack create to create a new
The first thing you’ll notice is that Spack prepends
r- to the front
of the package name. This is how Spack separates R package extensions
from the rest of the packages in Spack. Without this, we would end up
with package name collisions more frequently than we would like. For
instance, there are already packages for both:
Many popular programs written in C/C++ are later ported to R as a separate project.
The first thing you’ll need to add to your new package is a description.
The top of the homepage for
caret lists the following description:
Classification and Regression Training
Misc functions for training and plotting classification and regression models.
The first line is a short description (title) and the second line is a long description. In this case the description is only one line but often the description is several lines. Spack makes use of both short and long descriptions and convention is to use both when creating an R package.
If you look at the bottom of the page, you’ll see:
Please use the canonical form https://CRAN.R-project.org/package=caret to link to this page.
Please uphold the wishes of the CRAN admins and use https://cloud.r-project.org/package=caret as the homepage instead of https://cloud.r-project.org/web/packages/caret/index.html. The latter may change without notice.
As previously mentioned, the download URL for the latest release can be found by searching “Package source” on the homepage.
CRAN maintains a single webpage containing the latest release of every single package: https://cloud.r-project.org/src/contrib/
Of course, as soon as a new release comes out, the version you were using in your package is no longer available at that URL. It is moved to an archive directory. If you search for “Old sources”, you will find: https://cloud.r-project.org/src/contrib/Archive/caret
If you only specify the URL for the latest release, your package will
no longer be able to fetch that version as soon as a new release comes
out. To get around this, add the archive directory as a
Bioconductor packages are set up in a similar way to CRAN packages, but there are some very important distinctions. Bioconductor packages can be found at: https://bioconductor.org/. Bioconductor packages are R packages and so follow the same packaging scheme as CRAN packages. What is different is that Bioconductor itself is versioned and released. This scheme, using the Bioconductor package installer, allows further specification of the minimum version of R as well as further restrictions on the dependencies between packages than what is possible with the native R packaging system. Spack can not replicate these extra features and thus Bioconductor packages in Spack need to be managed as a group during updates in order to maintain package consistency with Bioconductor itself.
Another key difference is that, while previous versions of packages are
available, they are not available from a site that can be programmatically set,
list_url attribute can not be used. However, each package is also
available in a git repository, with branches corresponding to each Bioconductor
release. Thus, it is always possible to retrieve the version of any package
corresponding to a Bioconductor release simply by fetching the branch that
corresponds to the Bioconductor release of the package repository. For this
reason, spack Bioconductor R packages use the git repository, with the commit
of the respective branch used in the
version() attribute of the package.
cran and bioc attributes
Much like the
pypi attribute for python packages, due to the fact that R
packages are obtained from specific repositories, it is possible to set up shortcut
attributes that can be used to set
git. For example, the following
cran = "caret"
is equivalent to:
homepage = "https://cloud.r-project.org/package=caret"
url = "https://cloud.r-project.org/src/contrib/caret_6.0-86.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/caret"
Likewise, the following
bioc = "BiocVersion"
is equivalent to:
homepage = "https://bioconductor.org/packages/BiocVersion/"
git = "https://git.bioconductor.org/packages/BiocVersion"
Build system dependencies
As an extension of the R ecosystem, your package will obviously depend
on R to build and run. Normally, we would use
depends_on to express
this, but for R packages, we use
extends. This implies a special
dependency on R, which is used to set environment variables such as
R_LIBS uniformly. Since every R package needs this, the
base class contains:
Take a close look at the homepage for
caret. If you look at the
“Depends” section, you’ll notice that
caret depends on “R (≥ 3.2.0)”.
You should add this to your package like so:
depends_on("firstname.lastname@example.org:", type=("build", "run"))
R packages are often small and follow the classic Unix philosophy of doing one thing well. They are modular and usually depend on several other packages. You may find a single package with over a hundred dependencies. Luckily, R packages are well-documented and list all of their dependencies in the following sections:
As far as Spack is concerned, all 3 of these dependency types
type=("build", "run"), so you don’t have to worry
about the details. If you are curious what they mean,
https://github.com/spack/spack/issues/2951 has a pretty good summary:
Dependsis required and will cause those R packages to be attached, that is, their APIs are exposed to the user.
Importsloads packages so that the package importing these packages can access their APIs, while not being exposed to the user. When a user calls
library(foo)s/he attaches package
fooand all of the packages under
Depends. Any function in one of these package can be called directly as
bar(). If there are conflicts, user can also specify
pkgB::bar()to distinguish between them. Historically, there was only
Suggests, hence the confusing names. Today, maybe
Dependswould have been named
LinkingTois not perfect and there was recently an extensive discussion about API/ABI among other things on the R-devel mailing list among very skilled R developers:
Some packages also have a fourth section:
These are optional, rarely-used dependencies that a user might find useful. You should NOT add these dependencies to your package. R packages already have enough dependencies as it is, and adding optional dependencies can really slow down the concretization process. They can also introduce circular dependencies.
A fifth rarely used section is:
This means that the package can be used as an optional dependency for another package. Again, these packages should NOT be listed as dependencies.
Core, recommended, and non-core packages
If you look at “Depends”, “Imports”, and “LinkingTo”, you will notice 3 different types of packages:
If you look at the
caret homepage, you’ll notice a few dependencies
that don’t have a link to the package, like
utils. These packages are part of the core R distribution and are
tied to the R version installed. You can basically consider these to be
“R itself”. These are so essential to R that it would not make sense for
them to be updated via CRAN. If you did, you would basically get a different
version of R. Thus, they’re updated when R is updated.
You can find a list of these core libraries at: https://github.com/wch/r-source/tree/trunk/src/library
When you install R, there is an option called
This flag causes the R installation to include a few “Recommended” packages
(legacy term). They are for historical reasons quite tied to the core R
distribution, developed by the R core team or people closely related to it.
The R core distribution “knows” about these package, but they are indeed
distributed via CRAN. Because they’re distributed via CRAN, they can also be
updated between R version releases.
Spack explicitly adds the
--without-recommended-packages flag to prevent
the installation of these packages. Due to the way Spack handles package
activation (symlinking packages to the R installation directory),
pre-existing recommended packages will cause conflicts for already-existing
files. We could either not include these recommended packages in Spack and
require them to be installed through
we could not install them with R and let users choose the version of the
package they want to install. We chose the latter.
Since these packages are so commonly distributed with the R system, many developers may assume these packages exist and fail to list them as dependencies. Watch out for this.
You can find a list of these recommended packages at: https://github.com/wch/r-source/blob/trunk/share/make/vars.mk
These are packages that are neither “core” nor “recommended”. There are more than 10,000 of these packages hosted on CRAN alone.
For each of these package types, if you see that a specific version is required, for example, “lattice (≥ 0.20)”, please add this information to the dependency:
depends_on("email@example.com:", type=("build", "run"))
Some packages depend on non-R libraries for linking. Check out the r-stringi package for an example: https://cloud.r-project.org/package=stringi. If you search for the text “SystemRequirements”, you will see:
ICU4C (>= 52, optional)
This is how non-R dependencies are listed. Make sure to add these dependencies. The default dependency type should suffice.
Passing arguments to the installation
Some R packages provide additional flags that can be passed to
R CMD INSTALL, often to locate non-R dependencies.
is an example of this, and flags for linking to an MPI library. To pass
these to the installation command, you can override
mpi_name = self.spec["mpi"].name
# The type of MPI. Supported values are:
# OPENMPI, LAM, MPICH, MPICH2, or CRAY
if mpi_name == "openmpi":
Rmpi_type = "OPENMPI"
elif mpi_name == "mpich":
Rmpi_type = "MPICH2"
raise InstallError("Unsupported MPI type")
There is a similar
configure_vars function that can be overridden
to pass variables to the build.
Alternatives to Spack
CRAN hosts over 10,000 R packages, most of which are not in Spack. Many users may not need the advanced features of Spack, and may prefer to install R packages the normal way:
R will search CRAN for the
ggplot2 package and install all necessary
dependencies for you. If you want to update all installed R packages to
the latest release, you can use:
> update.packages(ask = FALSE)
This works great for users who have internet access, but those on an air-gapped cluster will find it easier to let Spack build a download mirror and install these packages for you.
Where Spack really shines is its ability to install non-R dependencies and link to them properly, something the R installation mechanism cannot handle.
For more information on installing R packages, see: https://stat.ethz.ch/R-manual/R-devel/library/utils/html/INSTALL.html
For more information on writing R packages, see: https://cloud.r-project.org/doc/manuals/r-release/R-exts.html
In particular, https://cloud.r-project.org/doc/manuals/r-release/R-exts.html#Package-Dependencies has a great explanation of the difference between Depends, Imports, and LinkingTo.